Automatic Detection of Left Ventricular Dilatation and Hypertrophy from Electrocardiograms Using Deep Learning
Author:
Affiliation:
1. School of Public Health, Graduate School of Medicine, The University of Tokyo
2. Department of Cardiovascular Medicine, The University of Tokyo Hospital
3. Department of Technology Management for Innovation, The University of Tokyo
Publisher
International Heart Journal (Japanese Heart Journal)
Subject
Cardiology and Cardiovascular Medicine,General Medicine
Link
https://www.jstage.jst.go.jp/article/ihj/63/5/63_22-132/_pdf
Reference32 articles.
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2. 2. Savarese G, Lund LH. Global public health burden of heart failure. Card Fail Rev 2017; 3: 7-11.
3. 3. Tsutsui H, Ide T, Ito H, et al. JCS/JHFS 2021 guideline focused update on diagnosis and treatment of acute and chronic heart failure. Circ J 2021; 85: 2252-91.
4. 4. Reed BN, Sueta CA. Stage B: what is the evidence for treatment of asymptomatic left ventricular dysfunction? Curr Cardiol Rev 2015; 11: 18-22.
5. 5. Echouffo-Tcheugui JB, Erqou S, Butler J, et al. Assessing the risk of progression from asymptomatic left ventricular dysfunction to overt heart failure: a systematic overview and meta-analysis. JACC Heart Fail 2016; 4: 237-48.
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1. Diagnostic accuracy of artificial intelligence in detecting left ventricular hypertrophy by electrocardiograph: a systematic review and meta-analysis;Scientific Reports;2024-07-10
2. Searching for the Best Machine Learning Algorithm for the Detection of Left Ventricular Hypertrophy from the ECG: A Review;Bioengineering;2024-05-15
3. One-shot screening: Utilization of a two-dimensional convolutional neural network for automatic detection of left ventricular hypertrophy using electrocardiograms;Computer Methods and Programs in Biomedicine;2024-04
4. Pediatric ECG-Based Deep Learning to Predict Left Ventricular Dysfunction and Remodeling;Circulation;2024-03-19
5. Prevention and treatment of hypertensive left ventricular hypertrophy;Current Opinion in Cardiology;2024-03-05
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